Manager Selection

AgPa #78: Hedge Funds – Man vs. Machine

Man vs. Machine: Comparing Discretionary and Systematic Hedge Fund Performance (2017)
Campbell R. Harvey, Sandy Rattray, Andrew Sinclair, Otto Van Hemert
The Journal of Portfolio Management 43(4), URL/SSRN

This week’s AGNOSTIC Paper examines the ongoing Man vs. Machine question in asset management at the example of hedge funds. The paper is therefore a predecessor to AgPa #21 that examines the same question for AI-powered mutual funds. The authors mention that there are still myths around systematic investing and many investors seem to have some kind of algorithm aversion. This is in-line with my own experiences, so I believe the paper fills an important gap for better education. In addition to that, the authors provide a practical framework to evaluate the performance and risks of hedge funds which I believe goes beyond the question of Man vs. Machine.

  • Macro hedge funds: systematic beat discretionary
  • Equity hedge funds: a draw between systematic and discretionary
  • Systematic and discretionary funds are quite similar
  • Hedge fund investing is more difficult than averages suggest

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AgPa #77: Too Much Passive Investing?

The Rise of Passive Investing and Active Mutual Fund Skill (2023)
Da Huang
SSRN Working Paper, URL

This week’s AGNOSTIC Paper is a quite recent working paper that examines the impact of passive investing on the US stock market. The debate about a potential tipping point when too many assets go passive is ongoing and often quite emotional. Depending on who you ask, you hear everything from “fundamentally broken” markets to the idea that we only need very few skilled active managers who compete for all the alpha. This week’s paper provides some interesting theoretical and empirical results on that matter.

  • Passive investing in the US grew tremendously
  • Passive investing forces unskilled managers to quit
  • Surviving active managers have more skill, but take less risk
  • We are probably not yet at the point of too much passive

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AgPa #75: Optimal Investment Committees

Optimal Design of Investment Committees (2023)
Bernd Scherer
The Journal of Asset Management, URL/SSRN

After a long break of almost exactly 3 months – I had several other tasks that required my intellectual capacity – it is time for a new AGNOSTIC Paper. This one examines the design and challenges of investment committees (ICs). Even more important, the author suggests a simple and powerful solution for some of their most common challenges. As someone who regularly enjoys the process of committee-based decision-making, I believe this week’s paper is quite powerful and offers a lot of valuable lessons for both investment managers and their clients.

  • Good theory: ICs ensure the same quality for all clients
  • Bad practice: ICs suffer from psychological biases
  • Solution: Anonymous member-portfolios

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AgPa #72: Machine-Reading of Private Equity Prospectuses

Limited Partners versus Unlimited Machines: Artificial Intelligence and the Performance of Private Equity Funds (2023)
Reiner Braun, Borja Fernández Tamayo, Florencio López-de-Silanes, Ludovic Phalippou, Natalia Sigrist
CEFS Research Paper, URL/SSRN

This week’s AGNOSTIC Paper is somewhat outside my major area of competence, but I think it is a good example where we are heading to in the investment industry. Over the last years, it became quite standard that investors use the latest tools of machine learning to analyze non-quantitative information like text or images at a scale that hasn’t been possible before. So far, however, the efforts were mostly focused on public markets. In their not yet published working paper, this week’s authors show that there seems to be also a lot of potential for such methods in private markets.

  • Portfolio Company, Management Team, Investment Opportunity – The most common words of PE-managers
  • The complexity of PE-fund documents is related to fundraising success and performance
  • Machine learning and text data helps to select PE-funds
  • The machines seem to pick up meaningful concepts

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AgPa #68: Machine-Learned Manager Selection (4/4)

A Cross-Sectional Machine Learning Approach for Hedge Fund Return Prediction and Selection (2021)
Wenbo Wu, Jiaqi Chen, Zhibin (Ben) Yang, Michael L. Tindall
Management Science 67(7), URL/SSRN

The fourth and at least for the moment final AGNOSTIC Paper on Machine Learned Manager Selection. After examining equity mutual funds in the last three papers, this week‘s authors provide an interesting out-of-sample test and explore machine learning models for selecting hedge funds. Importantly, this week‘s paper appeared in one of the leading business journals already back in 2021. This increases the likelihood that the results are actually robust and strengthens the evidence.

  • Machine learning helps to identify outperforming hedge funds
  • Risk measures and VIX-correlations are the most important features

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AgPa #67: Machine-Learned Manager Selection (3/4)

Selecting Mutual Funds from the Stocks They Hold: A Machine Learning Approach (2020)
Bin Li, Alberto G. Rossi
SSRN Working Paper, URL

The third AGNOSTIC Paper on the application of machine learning in manager selection. This week’s paper is very similar to AgPa #65 and AgPa #66, and again examines the data on US mutual funds. Despite somewhat different methodology, the results point in a similar direction. This, of course, increases the evidence that machine learning is actually useful for manager selection…

  • Machine learning helps to identify outperforming funds
  • The best and worst funds share common characteristics
  • Trading Frictions and Momentum are the most relevant variables

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AgPa #66: Machine-Learned Manager Selection (2/4)

Machine Learning and Fund Characteristics Help to Select Mutual Funds with Positive Alpha (2023)
Victor DeMiguel, Javier Gil-Bazo, Francisco J. Nogales, Andre A. P. Santos
SSRN Working Paper, URL

The second AGNOSTIC Paper on the application of machine learning in manager selection. This week’s paper follows essentially the same idea as Kaniel et al. (2022) in AgPa #65. The authors also examine a comprehensive sample of US mutual funds and although they use slightly different methodology, arrive at generally similar conclusions. This, of course, increases the evidence that machine learning is indeed helpful for manager selection…

  • Machine learning helps to identify outperforming funds
  • Past performance and measures of activeness are the most relevant variables
  • Given their alpha, machine-selected funds remain too small

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AgPa #65: Machine-Learned Manager Selection (1/4)

Machine-Learning the Skill of Mutual Fund Managers (2022)
Ron Kaniel, Zihan Lin, Markus Pelger, Stijn Van Nieuwerburgh
NBER Working Paper 29723, URL

To conclude the posts on manager selection, at least for the moment, I will dive into one of the most recent research frontiers in this area. Since the application of machine learning in investment management has been intensively studied among equities for more than three years now, it is not surprising that researchers also start to apply such algorithms to other asset classes. A natural candidate for this are equity mutual funds and this is exactly where this and the next four week’s AGNOSTIC Papers come in.

  • Machine learning helps to identify outperforming funds
  • Less is more – not all information is necessary
  • Alpha is easier to predict than total returns

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AgPa #64: Fund Manager Multitasking

Managerial Multitasking in the Mutual Fund Industry (2023)
Vikas Agarwal, Linlin Ma, Kevin Mullally
Financial Analysts Journal 79(2), URL/SSRN

Some days ago, I came across yet another interesting study on manager selection. The idea of this week’s AGNOSTIC Paper is very straight forward. When you hire a fund manager, you want this person to focus on your money and not do much else. Probably no one would agree to a surgery where the surgeon operates on five patients at the same time. So why hire a fund manager who manages more than one fund?

  • Manager multitasking strongly increased from 1990 to 2018
  • Managers who start multitasking tend to have better track records
  • Fund performance decreases significantly after managers start multitasking
  • The number of managed funds amplifies the effect of multitasking
  • Investors put less money into existing funds of multitasking managers

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AgPa #63: Fire the Winners and Hire the Losers

The Folly of Hiring Winners and Firing Losers (2018)
Rob Arnott, Vitali Kalesnik, Lillian Wu
The Journal of Portfolio Management Fall 2018, 45 (1), URL/research affiliates

I am still in my research on manager selection, so apologies to everyone who doesn’t find that too interesting. We already touched the question on what to do with underperforming managers in AgPa #59 and #60. This week’s AGNOSTIC Paper, however, examines this problem somewhat more generally and delivers some really simple (but psychologically hard-to-execute) common-sense conclusions.

  • Current winners tend to be future losers
  • High fees are the most reliable way to underperform
  • Investors should use factor exposures and valuations to evaluate fund managers

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